How to leverage Spark with Aerospike NoSQL Database to get real time insights. Session was delivered at "Big Data, Max Speed @ Minimal Cost" Meetup at Nielsen R&D Center in Tel Aviv, March 4, 2020.
Getting Started with Apache Spark and Alluxio for Blazingly Fast AnalyticsAlluxio, Inc.
Alluxio Austin Meetup
Aug 15, 2019
Speaker: Bin Fan
Apache Spark and Alluxio are cousin open source projects that originated from UC Berkeley’s AMPLab. Running Spark with Alluxio is a popular stack particularly for hybrid environments. In this session, I will briefly introduce Apache Spark and Alluxio, share the top ten tips for performance tuning for real-world workloads, and demo Alluxio with Spark.
HPE provides optimized server architectures for Hadoop including the Apollo 4200 server which offers high storage density. HPE also offers a reference architecture for Hadoop that separates compute and storage resources for better performance, using optimized servers like Moonshot for processing and Apollo for storage. Additionally, HPE contributes to Apache Spark through HP Labs to improve efficiency and scale of memory and performance.
Dancing Elephants: Working with Object Storage in Apache Spark and HiveSteve Loughran
A talk looking at the intricate details of working with an object store from Hadoop, Hive, Spark, etc, why the "filesystem" metaphor falls down, and what work myself and others have been up to to try and fix things
Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit
If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong.
This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data.
It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application.
If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.
Berlin Buzzwords 2017 talk: A look at what our storage models, metaphors and APIs are, showing how we need to rethink the Posix APIs to work with object stores, while looking at different alternatives for local NVM.
This is the unabridged talk; the BBuzz talk was 20 minutes including demo and questions, so had ~half as many slides
Apache Spark and Object Stores —for London Spark User GroupSteve Loughran
The March 2017 version of the "Apache Spark and Object Stores", includes coverage of the Staging Committer. If you'd been at the talk you'd have seen the projector fail just before the demo. It worked earlier! Honest!
Improving Python and Spark (PySpark) Performance and InteroperabilityWes McKinney
Slides from Spark Summit East 2017 — February 9, 2017 in Boston. Discusses ongoing development work to accelerate Python-on-Spark performance using Apache Arrow and other tools
Getting Started with Apache Spark and Alluxio for Blazingly Fast AnalyticsAlluxio, Inc.
Alluxio Austin Meetup
Aug 15, 2019
Speaker: Bin Fan
Apache Spark and Alluxio are cousin open source projects that originated from UC Berkeley’s AMPLab. Running Spark with Alluxio is a popular stack particularly for hybrid environments. In this session, I will briefly introduce Apache Spark and Alluxio, share the top ten tips for performance tuning for real-world workloads, and demo Alluxio with Spark.
HPE provides optimized server architectures for Hadoop including the Apollo 4200 server which offers high storage density. HPE also offers a reference architecture for Hadoop that separates compute and storage resources for better performance, using optimized servers like Moonshot for processing and Apollo for storage. Additionally, HPE contributes to Apache Spark through HP Labs to improve efficiency and scale of memory and performance.
Dancing Elephants: Working with Object Storage in Apache Spark and HiveSteve Loughran
A talk looking at the intricate details of working with an object store from Hadoop, Hive, Spark, etc, why the "filesystem" metaphor falls down, and what work myself and others have been up to to try and fix things
Spark and Object Stores —What You Need to Know: Spark Summit East talk by Ste...Spark Summit
If you are running Apache Spark in cloud environments, Object Stores —such as Amazon S3 or Azure WASB— are a core part of your system. What you can’t do is treat them like “just another filesystem” —do that and things will, eventually, go horribly wrong.
This talk looks at the object stores in the cloud infrastructures, including underlying architectures., compares them to what a “real filesystem” is expected to do and shows how to use object stores efficiently and safely as sources of and destinations of data.
It goes into depth on recent “S3a” work, showing how including improvements in performance, security, functionality and measurement —and demonstrating how to use make best use of it from a spark application.
If you are planning to deploy Spark in cloud, or doing so today: this is information you need to understand. The performance of you code and integrity of your data depends on it.
Berlin Buzzwords 2017 talk: A look at what our storage models, metaphors and APIs are, showing how we need to rethink the Posix APIs to work with object stores, while looking at different alternatives for local NVM.
This is the unabridged talk; the BBuzz talk was 20 minutes including demo and questions, so had ~half as many slides
Apache Spark and Object Stores —for London Spark User GroupSteve Loughran
The March 2017 version of the "Apache Spark and Object Stores", includes coverage of the Staging Committer. If you'd been at the talk you'd have seen the projector fail just before the demo. It worked earlier! Honest!
Improving Python and Spark (PySpark) Performance and InteroperabilityWes McKinney
Slides from Spark Summit East 2017 — February 9, 2017 in Boston. Discusses ongoing development work to accelerate Python-on-Spark performance using Apache Arrow and other tools
This document discusses accelerating Spark workloads on Amazon S3 using Alluxio. It describes the challenges of running Spark interactively on S3 due to its eventual consistency and expensive metadata operations. Alluxio provides a data caching layer that offers strong consistency, faster performance, and API compatibility with HDFS and S3. It also allows data outside of S3 to be analyzed. The document demonstrates how to bootstrap Alluxio on an AWS EMR cluster to accelerate Spark workloads running on S3.
This document discusses enterprise-grade big data solutions from HPE. It outlines HPE's reference architecture for big data workloads including components like data lakes, data warehouses, archival storage, event processing, and in-memory analytics. It also discusses HPE's investments in Hortonworks and collaboration to optimize Hadoop for performance. The document promotes attending an HPE session at the Hadoop Summit on modernizing data warehouses and visiting the HPE booth for demos and a trivia game.
Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
High Performance Python on Apache SparkWes McKinney
This document contains the slides from a presentation given by Wes McKinney on high performance Python on Apache Spark. The presentation discusses why Python is an important and productive language, defines what is meant by "high performance Python", and explores techniques for building fast Python software such as embracing limitations of the Python interpreter and using native data structures and compiled extensions where needed. Specific examples are provided around control flow, reading CSV files, and the importance of efficient in-memory data structures.
This document discusses Hive on Spark, which allows Apache Hive queries to run on Apache Spark. It provides background on Hive, Spark, and their limitations. Hive on Spark was developed by the Hive community to leverage Spark's more efficient execution while maintaining compatibility. Examples are given of how simple and join queries are translated from Hive operations to Spark transformations and actions. Improvements to Spark needed to better support Hive are also outlined. The author thanks contributors from various organizations working on Hive on Spark.
Hive on spark is blazing fast or is it finalHortonworks
This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.
Advancing GPU Analytics with RAPIDS Accelerator for Spark and AlluxioAlluxio, Inc.
This document discusses accelerating Apache Spark workloads using RAPIDS Accelerator for Spark and Alluxio. It provides an introduction to RAPIDS Accelerator for Spark, shows significant performance gains over CPU-only Spark, and discusses combining GPU acceleration with Alluxio for optimized performance and cost on cloud datasets. Configuration options for RAPIDS and Alluxio are also covered.
Query Anything, Anywhere with KubernetesAlluxio, Inc.
Alluxio Bay Area Meetup @ Galvanize | SF
Aug 20, 2019
Interactive Analytics in the Cloud with Presto and Alluxio
Speaker:
Kamil Bajda-Pawlikowski, Co-founder / CTO, Starburst Data
Spark Summit EU talk by Debasish Das and Pramod NarasimhaSpark Summit
This document describes how Apache Spark and Apache Lucene can be used together for near-real-time predictive model building. It discusses representing streaming device data in Lucene documents that are indexed for fast search and retrieval. A framework called Trapezium is used to build batch, streaming, and API services on top of Spark and Lucene. It shows how to index large datasets in Lucene efficiently using Spark and analyze retrieved devices to generate statistical and predictive models.
Presto + Alluxio on steroids a romantic drama on Production with happy endAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Presto + Alluxio on steroids a romantic drama on Production with happy end
Speaker:
Danny Linden, Ryte
For more Alluxio events: https://www.alluxio.io/events/
A review of the state of cloud store integration with the Hadoop stack in 2018; including S3Guard, the new S3A committers and S3 Select.
Presented at Dataworks Summit Berlin 2018, where the demos were live.
CtrlS DR on Demand framework provides disaster recovery capabilities at a fraction of the cost of maintaining a full secondary DR site. It ensures critical applications and data can be restored and made available within hours of an outage occurring at the primary site. The framework defines the full DR strategy including architecture, initial setup, data replication processes, and activation of the DR site hosted within CtrlS's private cloud using their network and storage infrastructure and DR specialists. This approach saves over 40% compared to maintaining a traditional cold, warm or hot site DR solution.
Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks
The document discusses different strategies for horizontally scaling databases, including simple sharding, hashed sharding, and master-slave architectures. It describes Aerospike's approach of "smart partitioning", which balances data automatically, hides complexity from clients, and provides redundancy and failover. The key advantages are linear scalability, high availability even during maintenance, and the ability to handle catastrophic failures through multi-datacenter replication that can withstand outages and disasters.
This document discusses Hive on Spark, including background on Hive, Spark, and the Shark project. It describes how Hive on Spark keeps the same physical abstraction as Hive on Tez/MR to be architecturally compatible. Examples are provided of how a simple query and join query are executed in MapReduce and Spark formats. Improvements to Spark for reduce-side joins and remote Spark contexts are also discussed.
Have you decided on Amazon Redshift as your data warehouse but want to learn the latest tips and tricks to get started? Watch our webinar on Tuesday, August 29th to learn how to get started and how using Redshift can help you quickly and easily analyze your data to make business critical decisions.
Webinar | Getting Started With Amazon Redshift SpectrumMatillion
In the first webinar in our Amazon Redshift Spectrum Series, learn more about getting started with Spectrum.
Spectrum allows you to use the analytic capabilities of Amazon Redshift beyond the data which is in your data warehouse to query large amounts of semistructured and structured data in your “data lake,” without having to load or transform it into Amazon Redshift.
In this webinar learn:
- What is Amazon Redshift Spectrum?
- How to set up AWS Security for Amazon Redshift Spectrum
- What are external schemas and external tables
- How to query S3 data with Redshift SQL via Amazon Redshift Spectrum using Matillion ETL
This document discusses improving the reliability and availability of Hadoop clusters. It notes that while Hadoop is taking on more database-like features, the uptime of many Hadoop clusters and lack of SLAs is still an afterthought. It proposes separating computing and storage to improve availability like cloud Hadoop offerings do. It also suggests building KPIs and monitoring around Hadoop clusters similar to how many companies monitor data warehouses. Centralizing Hadoop infrastructure management into a "Big Data as a Service" model is presented as another way to improve reliability.
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
This document discusses best practices for optimizing Apache Spark applications. It covers techniques for speeding up file loading, optimizing file storage and layout, identifying bottlenecks in queries, dealing with many partitions, using datasource tables, managing schema inference, file types and compression, partitioning and bucketing files, managing shuffle partitions with adaptive execution, optimizing unions, using the cost-based optimizer, and leveraging the data skipping index. The presentation aims to help Spark developers apply these techniques to improve performance.
Distributed Data Storage & Streaming for Real-time Decisioning Using Kafka, S...HostedbyConfluent
Real-time connectivity of databases and systems is critical in enterprises adopting digital transformation to support super-fast decisioning to drive applications like fraud detection, digital payments, recommendation engines. This talk will focus on the many functions that database streaming serves with Kafka, Spark and Aerospike. We will explore how to eliminate the wall between transaction processing and analytics by synthesizing streaming data with system of record data, to gain key insights in real-time.
This document discusses Oracle's SPARC systems and their ability to modernize legacy Unix applications and provide a path to the cloud. It describes how SPARC systems offer a modern, cloud-ready infrastructure that can leverage existing investments while improving security, capacity, and flexibility. It provides examples of SPARC solutions that delivered benefits like reduced costs, increased throughput, and scalability for customers in various industries.
This document discusses accelerating Spark workloads on Amazon S3 using Alluxio. It describes the challenges of running Spark interactively on S3 due to its eventual consistency and expensive metadata operations. Alluxio provides a data caching layer that offers strong consistency, faster performance, and API compatibility with HDFS and S3. It also allows data outside of S3 to be analyzed. The document demonstrates how to bootstrap Alluxio on an AWS EMR cluster to accelerate Spark workloads running on S3.
This document discusses enterprise-grade big data solutions from HPE. It outlines HPE's reference architecture for big data workloads including components like data lakes, data warehouses, archival storage, event processing, and in-memory analytics. It also discusses HPE's investments in Hortonworks and collaboration to optimize Hadoop for performance. The document promotes attending an HPE session at the Hadoop Summit on modernizing data warehouses and visiting the HPE booth for demos and a trivia game.
Dancing elephants - efficiently working with object stores from Apache Spark ...DataWorks Summit
As Hadoop applications move into cloud deployments, object stores become more and more the source and destination of data. But object stores are not filesystems: sometimes they are slower; security is different,
What are the secret settings to get maximum performance from queries against data living in cloud object stores? That's at the filesystem client, the file format and the query engine layers? It's even how you lay out the files —the directory structure and the names you give them.
We know these things, from our work in all these layers, from the benchmarking we've done —and the support calls we get when people have problems. And now: we'll show you.
This talk will start from the ground up "why isn't an object store a filesystem?" issue, showing how that breaks fundamental assumptions in code, and so causes performance issues which you don't get when working with HDFS. We'll look at the ways to get Apache Hive and Spark to work better, looking at optimizations which have been done to enable this —and what work is ongoing. Finally, we'll consider what your own code needs to do in order to adapt to cloud execution.
High Performance Python on Apache SparkWes McKinney
This document contains the slides from a presentation given by Wes McKinney on high performance Python on Apache Spark. The presentation discusses why Python is an important and productive language, defines what is meant by "high performance Python", and explores techniques for building fast Python software such as embracing limitations of the Python interpreter and using native data structures and compiled extensions where needed. Specific examples are provided around control flow, reading CSV files, and the importance of efficient in-memory data structures.
This document discusses Hive on Spark, which allows Apache Hive queries to run on Apache Spark. It provides background on Hive, Spark, and their limitations. Hive on Spark was developed by the Hive community to leverage Spark's more efficient execution while maintaining compatibility. Examples are given of how simple and join queries are translated from Hive operations to Spark transformations and actions. Improvements to Spark needed to better support Hive are also outlined. The author thanks contributors from various organizations working on Hive on Spark.
Hive on spark is blazing fast or is it finalHortonworks
This presentation was given at the Strata + Hadoop World, 2015 in San Jose.
Apache Hive is the most popular and most widely used SQL solution for Hadoop. To keep pace with Hadoop’s increasingly vital role in the Enterprise, Hive has transformed from a batch-only, high-latency system into a modern SQL engine capable of both batch and interactive queries over large datasets. Hive’s momentum is accelerating: With Spark integration and a shift to in-memory processing on the horizon, Hive continues to expand the boundaries of Big Data.
In this talk the speakers examined Hive performance, past, present and future. In particular they looked at Hive’s origins as a petabyte scale SQL engine.
Through some numbers and graphs, they showed how Hive became 100x faster by moving beyond MapReduce, by vectorizing execution and by introducing a cost-based optimizer.
They detailed and discussed the challenges of scalable SQL on Hadoop.
The looked into Hive’s sub-second future, powered by LLAP and Hive on Spark.
And showed just how fast Hive on Spark really is.
Advancing GPU Analytics with RAPIDS Accelerator for Spark and AlluxioAlluxio, Inc.
This document discusses accelerating Apache Spark workloads using RAPIDS Accelerator for Spark and Alluxio. It provides an introduction to RAPIDS Accelerator for Spark, shows significant performance gains over CPU-only Spark, and discusses combining GPU acceleration with Alluxio for optimized performance and cost on cloud datasets. Configuration options for RAPIDS and Alluxio are also covered.
Query Anything, Anywhere with KubernetesAlluxio, Inc.
Alluxio Bay Area Meetup @ Galvanize | SF
Aug 20, 2019
Interactive Analytics in the Cloud with Presto and Alluxio
Speaker:
Kamil Bajda-Pawlikowski, Co-founder / CTO, Starburst Data
Spark Summit EU talk by Debasish Das and Pramod NarasimhaSpark Summit
This document describes how Apache Spark and Apache Lucene can be used together for near-real-time predictive model building. It discusses representing streaming device data in Lucene documents that are indexed for fast search and retrieval. A framework called Trapezium is used to build batch, streaming, and API services on top of Spark and Lucene. It shows how to index large datasets in Lucene efficiently using Spark and analyze retrieved devices to generate statistical and predictive models.
Presto + Alluxio on steroids a romantic drama on Production with happy endAlluxio, Inc.
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Presto + Alluxio on steroids a romantic drama on Production with happy end
Speaker:
Danny Linden, Ryte
For more Alluxio events: https://www.alluxio.io/events/
A review of the state of cloud store integration with the Hadoop stack in 2018; including S3Guard, the new S3A committers and S3 Select.
Presented at Dataworks Summit Berlin 2018, where the demos were live.
CtrlS DR on Demand framework provides disaster recovery capabilities at a fraction of the cost of maintaining a full secondary DR site. It ensures critical applications and data can be restored and made available within hours of an outage occurring at the primary site. The framework defines the full DR strategy including architecture, initial setup, data replication processes, and activation of the DR site hosted within CtrlS's private cloud using their network and storage infrastructure and DR specialists. This approach saves over 40% compared to maintaining a traditional cold, warm or hot site DR solution.
Apache Hadoop 3 is coming! As the next major milestone for hadoop and big data, it attracts everyone's attention as showcase several bleeding-edge technologies and significant features across all components of Apache Hadoop: Erasure Coding in HDFS, Docker container support, Apache Slider integration and Native service support, Application Timeline Service version 2, Hadoop library updates and client-side class path isolation, etc. In this talk, first we will update the status of Hadoop 3.0 releasing work in apache community and the feasible path through alpha, beta towards GA. Then we will go deep diving on each new feature, include: development progress and maturity status in Hadoop 3. Last but not the least, as a new major release, Hadoop 3.0 will contain some incompatible API or CLI changes which could be challengeable for downstream projects and existing Hadoop users for upgrade - we will go through these major changes and explore its impact to other projects and users.
Speaker: Sanjay Radia, Founder and Chief Architect, Hortonworks
The document discusses different strategies for horizontally scaling databases, including simple sharding, hashed sharding, and master-slave architectures. It describes Aerospike's approach of "smart partitioning", which balances data automatically, hides complexity from clients, and provides redundancy and failover. The key advantages are linear scalability, high availability even during maintenance, and the ability to handle catastrophic failures through multi-datacenter replication that can withstand outages and disasters.
This document discusses Hive on Spark, including background on Hive, Spark, and the Shark project. It describes how Hive on Spark keeps the same physical abstraction as Hive on Tez/MR to be architecturally compatible. Examples are provided of how a simple query and join query are executed in MapReduce and Spark formats. Improvements to Spark for reduce-side joins and remote Spark contexts are also discussed.
Have you decided on Amazon Redshift as your data warehouse but want to learn the latest tips and tricks to get started? Watch our webinar on Tuesday, August 29th to learn how to get started and how using Redshift can help you quickly and easily analyze your data to make business critical decisions.
Webinar | Getting Started With Amazon Redshift SpectrumMatillion
In the first webinar in our Amazon Redshift Spectrum Series, learn more about getting started with Spectrum.
Spectrum allows you to use the analytic capabilities of Amazon Redshift beyond the data which is in your data warehouse to query large amounts of semistructured and structured data in your “data lake,” without having to load or transform it into Amazon Redshift.
In this webinar learn:
- What is Amazon Redshift Spectrum?
- How to set up AWS Security for Amazon Redshift Spectrum
- What are external schemas and external tables
- How to query S3 data with Redshift SQL via Amazon Redshift Spectrum using Matillion ETL
This document discusses improving the reliability and availability of Hadoop clusters. It notes that while Hadoop is taking on more database-like features, the uptime of many Hadoop clusters and lack of SLAs is still an afterthought. It proposes separating computing and storage to improve availability like cloud Hadoop offerings do. It also suggests building KPIs and monitoring around Hadoop clusters similar to how many companies monitor data warehouses. Centralizing Hadoop infrastructure management into a "Big Data as a Service" model is presented as another way to improve reliability.
Lessons from the Field: Applying Best Practices to Your Apache Spark Applicat...Databricks
This document discusses best practices for optimizing Apache Spark applications. It covers techniques for speeding up file loading, optimizing file storage and layout, identifying bottlenecks in queries, dealing with many partitions, using datasource tables, managing schema inference, file types and compression, partitioning and bucketing files, managing shuffle partitions with adaptive execution, optimizing unions, using the cost-based optimizer, and leveraging the data skipping index. The presentation aims to help Spark developers apply these techniques to improve performance.
Distributed Data Storage & Streaming for Real-time Decisioning Using Kafka, S...HostedbyConfluent
Real-time connectivity of databases and systems is critical in enterprises adopting digital transformation to support super-fast decisioning to drive applications like fraud detection, digital payments, recommendation engines. This talk will focus on the many functions that database streaming serves with Kafka, Spark and Aerospike. We will explore how to eliminate the wall between transaction processing and analytics by synthesizing streaming data with system of record data, to gain key insights in real-time.
This document discusses Oracle's SPARC systems and their ability to modernize legacy Unix applications and provide a path to the cloud. It describes how SPARC systems offer a modern, cloud-ready infrastructure that can leverage existing investments while improving security, capacity, and flexibility. It provides examples of SPARC solutions that delivered benefits like reduced costs, increased throughput, and scalability for customers in various industries.
AWS April 2016 Webinar Series - Best Practices for Apache Spark on AWSAmazon Web Services
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges.
In this webinar, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures and best practices to quickly create Spark clusters using Amazon Elastic MapReduce (EMR), and ways to use Spark with Amazon Redshift, Amazon DynamoDB, Amazon Kinesis, and other big data applications in the Apache Hadoop ecosystem.
Learning Objectives:
Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing
How to deploy and tune scalable clusters running Spark on Amazon EMR
How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3
Common architectures to leverage Spark with DynamoDB, Redshift, Kinesis, and more
Vectorized Deep Learning Acceleration from Preprocessing to Inference and Tra...Databricks
This talk presents how we accelerated deep learning processing from preprocessing to inference and training on Apache Spark in SK Telecom. In SK Telecom, we have half a Korean population as our customers. To support them, we have 400,000 cell towers, which generates logs with geospatial tags.
Data Science & Best Practices for Apache Spark on Amazon EMRAmazon Web Services
Organizations need to perform increasingly complex analysis on their data — streaming analytics, ad-hoc querying and predictive analytics — in order to get better customer insights and actionable business intelligence. However, the growing data volume, speed, and complexity of diverse data formats make current tools inadequate or difficult to use. Apache Spark has recently emerged as the framework of choice to address these challenges. Spark is a general-purpose processing framework that follows a DAG model and also provides high-level APIs, making it more flexible and easier to use than MapReduce. Thanks to its use of in-memory datasets (RDDs), embedded libraries, fault-tolerance, and support for a variety of programming languages, Apache Spark enables developers to implement and scale far more complex big data use cases, including real-time data processing, interactive querying, graph computations and predictive analytics. In this session, we present a technical deep dive on Spark running on Amazon EMR. You learn why Spark is great for ad-hoc interactive analysis and real-time stream processing, how to deploy and tune scalable clusters running Spark on Amazon EMR, how to use EMRFS with Spark to query data directly in Amazon S3, and best practices and patterns for Spark on Amazon EMR.
Configuring storage. The slides to this webinar cover how to configure storage for Aerospike. It includes a discussion of how Aerospike uses Flash/SSDs and how to get the best performance out of them.
Find the full webinar with audio here - http://www.aerospike.com/webinars
Amazon Aurora is a MySQL and PostgreSQL compatible relational database built for the cloud, that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. AWS Database Migration Service helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. In this session, we explore features of Amazon Aurora and demonstrate database migration using the AWS Database Migration Service.
Dr. Ike Nassi, Founder, TidalScale at MLconf NYC - 4/15/16MLconf
Scaling Spark – Vertically: The mantra of Spark technology is divide and conquer, especially for problems too big for a single computer. The more you divide a problem across worker nodes, the more total memory and processing parallelism you can exploit. This comes with a trade-off. Splitting applications and data across multiple nodes is nontrivial, and more distribution results in more network traffic which becomes a bottleneck. Can you achieve scale and parallelism without those costs?
We’ll show results of a variety of Spark application domains including structured data, graph processing and common machine learning in a single, high-capacity scaled-up system versus a more distributed approach and discuss how virtualization can be used to define node size flexibly, achieving the best balance for Spark performance.
The document discusses Oracle's SPARC servers and Solaris operating system. It highlights the new SPARC T4 servers as providing up to 5x faster performance than previous T3 servers. It also promotes the SPARC SuperCluster as the fastest general purpose platform, capable of outperforming IBM and HP systems. Oracle positions its SPARC/Solaris products as the best foundation for enterprise cloud computing and engineered to work optimally with Oracle software.
Spectrum Scale - Diversified analytic solution based on various storage servi...Wei Gong
This slides describe diversified analytic solutions based on Spectrum Scale with various deployment mode, such as storage rich-server, share storage, IBM DeepFlash 150 and Elastic Storage Server. It deep dives several advanced data management features and solutions for BD&A workload derived from Spectrum Scale.
Organizations need to perform increasingly complex analysis on data — streaming analytics, ad-hoc querying, and predictive analytics — in order to get better customer insights and actionable business intelligence. Apache Spark has recently emerged as the framework of choice to address many of these challenges. In this session, we show you how to use Apache Spark on AWS to implement and scale common big data use cases such as real-time data processing, interactive data science, predictive analytics, and more. We will talk about common architectures, best practices to quickly create Spark clusters using Amazon EMR, and ways to integrate Spark with other big data services in AWS.
Learning Objectives:
• Learn why Spark is great for ad-hoc interactive analysis and real-time stream processing.
• How to deploy and tune scalable clusters running Spark on Amazon EMR.
• How to use EMR File System (EMRFS) with Spark to query data directly in Amazon S3.
• Common architectures to leverage Spark with Amazon DynamoDB, Amazon Redshift, Amazon Kinesis, and more.
The document provides details about Oracle's SPARC S7 servers and SPARC S7 processor. It discusses the key features and capabilities of the SPARC S7 processor, including software-in-silicon features for security, compression, and analytics acceleration. It also provides specifications for the SPARC S7-2 and SPARC S7-2L server models, which are based on the SPARC S7 processor.
Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction wi...Databricks
In this talk, we will present how we analyze, predict, and visualize network quality data, as a spark AI use case in a telecommunications company. SK Telecom is the largest wireless telecommunications provider in South Korea with 300,000 cells and 27 million subscribers. These 300,000 cells generate data every 10 seconds, the total size of which is 60TB, 120 billion records per day.
In order to address previous problems of Spark based on HDFS, we have developed a new data store for SparkSQL consisting of Redis and RocksDB that allows us to distribute and store these data in real time and analyze it right away, We were not satisfied with being able to analyze network quality in real-time, we tried to predict network quality in near future in order to quickly detect and recover network device failures, by designing network signal pattern-aware DNN model and a new in-memory data pipeline from spark to tensorflow.
In addition, by integrating Apache Livy and MapboxGL to SparkSQL and our new store, we have built a geospatial visualization system that shows the current population and signal strength of 300,000 cells on the map in real time.
Riccardo Romani from Oracle Italy discusses migrating workloads to Oracle's SPARC Cloud Infrastructure as a Service (IaaS). Key points include:
1) Oracle offers an easy "lift and shift" migration of workloads to its SPARC Cloud IaaS using standard virtualization.
2) Customers gain access to additional Oracle Cloud services like databases, storage, analytics while Oracle handles operations and support.
3) Performance tests show SPARC Cloud delivers up to 1.6-6.1x better performance than AWS for various workloads due to hardware acceleration features.
Oracle hardware includes a full-suite of scalable engineered systems, servers, and storage that enable enterprises to optimize application and database performance, protect crucial data, and lower costs.
With Oracle, customers have freedom from the complexity of having multiple databases, analytics tools, and machine learning environments. Oracle's data management platform makes it easier and faster for application developers to create microservices-based applications with multiple data types.
Oracle provides a comprehensive cloud infrastructure platform with compute, storage, networking and database services. Key features include fast NVMe SSD storage both locally and network attached, high performance bare metal and VM instances with GPU and AMD EPYC options, autonomous database services, and advanced networking capabilities like low latency and RDMA. Oracle's regional architecture and dedicated fast interconnects enable high availability across availability domains and regions.
This document provides an overview of Apache Spark, including:
- Apache Spark is a next generation data processing engine for Hadoop that allows for fast in-memory processing of huge distributed and heterogeneous datasets.
- Spark offers tools for data science and components for data products and can be used for tasks like machine learning, graph processing, and streaming data analysis.
- Spark improves on MapReduce by being faster, allowing parallel processing, and supporting interactive queries. It works on both standalone clusters and Hadoop clusters.
This document provides an overview of Apache Spark, including:
- Apache Spark is a next generation data processing engine for Hadoop that allows for fast in-memory processing of huge distributed and heterogeneous datasets.
- Spark offers tools for data science and components for data products and can be used for tasks like machine learning, graph processing, and streaming data analysis.
- Spark improves on MapReduce by being faster, allowing parallel processing, and supporting interactive queries. It works on both standalone clusters and Hadoop clusters.
Aerospike meetup july 2019 | Big Data DemystifiedOmid Vahdaty
Building a low latency (sub millisecond), high throughput database that can handle big data AND linearly scale is not easy - but we did it anyway...
In this session we will get to know Aerospike, an enterprise distributed primary key database solution.
- We will do an introduction to Aerospike - basic terms, how it works and why is it widely used in mission critical systems deployments.
- We will understand the 'magic' behind Aerospike ability to handle small, medium and even Petabyte scale data, and still guarantee predictable performance of sub-millisecond latency
- We will learn how Aerospike devops is different than other solutions in the market, and see how easy it is to run it on cloud environments as well as on premise.
We will also run a demo - showing a live example of the performance and self-healing technologies the database have to offer.
Similar to Aerospike Meetup - Real Time Insights using Spark with Aerospike - Zohar - 04 March 2020 (20)
Aerospike Meetup - Introduction - Ami - 04 March 2020Aerospike
Introduction to Aerospike NoSQL Database. Session was delivered at "Big Data, Max Speed @ Minimal Cost" Meetup at Nielsen R&D Center in Tel Aviv, March 4, 2020.
Aerospike Meetup - Nielsen Customer Story - Alex - 04 March 2020Aerospike
Aerospike at Nielsen customer story. Session was delivered at "Big Data, Max Speed @ Minimal Cost" Meetup at Nielsen R&D Center in Tel Aviv, March 4, 2020.
Aerospike Roadmap Overview - Meetup Dec 2019Aerospike
The document provides a summary of updates to Aerospike Enterprise Edition from May 2019 to December 2019, as well as planned updates through 2020. Key updates include adding compression, supporting nested data types and bitwise operations, improving scan and query performance, and adding capabilities like pagination. Planned updates focus on enhancing cross data center replication, secondary indexes, and the client-server protocol.
A presentation by Zohar Elkayam, Solutions Architect at Aerospike Israel from the IronSource meetup in Israel (December 2019).
The topic of this talk was Nested CDT (list and map) improvements in Aerospike version 4.6 and 4.7.
This included an explanation about data modeling and code samples of such implementation.
Aerospike Data Modeling - Meetup Dec 2019Aerospike
This is a presentation done by Ronen Botzer, the Director of Product at Aerospike as part of the IronSource meetup in Israel (December 2019).
In this talk, Ronen explained how to use nested CDTs and Bitwise operations in order to manage user segmentation and to create a proper data model.
JDBC Driver for Aerospike - Meetup Dec 2019Aerospike
The document describes a JDBC driver that allows SQL queries to be run against Aerospike databases. The driver provides SQL compliance by mapping SQL statements to the appropriate Aerospike operations. It supports statements like SELECT, INSERT, UPDATE, DELETE as well as functions, aggregation, JOINs and more. Future plans include improving performance, adding support for additional data types and operations, and deploying to a public repository. The goal is to provide a standard SQL interface for integrating Aerospike with various SQL-based tools and applications.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257